Sensors are used across different applications to capture information. Sensor data captured by the sensor may comprise various types of information. The information may either be sensitive, non-sensitive or context information. The sensor data may be processed for determining outliers related to the sensor data. The outliers may indicate critical information present in the sensor data.
For an example, outliers of Electrocardiogram (ECG) data may indicate abnormal pattern related to heart activity and may thus indicate a heart disease. The outliers of ECG data may be analyzed to determine criticality of a heart condition. Thus, critical data and non-critical data of ECG of a patient may be transmitted with different reliability, information update rate and priority level with reduced communication cost and energy.
Different sensors are used for analyzing different kinds of activity and different data processing techniques are used based on the type of data. Further, different outlier detection techniques are used based on features of the sensor data. Thus, it is always required to know about the signal dynamics of sensor data to be processed in order to derive information out of the sensor data as well as derive anomaly and use it further.